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Deep-learning-based beam position estimation with a photon-counting camera in free-space optical communications.

Xialin Liu, Ying Guo, Jun Huang

    Optics Express
    |December 19, 2025
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    Summary
    This summary is machine-generated.

    A new deep-learning method enhances deep-space optical sensing by accurately estimating beacon beam centers. This deep-learning-based super-resolution beam position estimator (DSRBPE) achieves sub-pixel precision even with noisy, weak signals.

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    Area of Science:

    • Optical sensing and communication
    • Deep learning applications
    • Photon detection

    Background:

    • Photon-counting cameras offer high sensitivity and time resolution for deep-space applications.
    • Accurate estimation of beacon beam centers is crucial but difficult due to photon noise and fluctuations.

    Purpose of the Study:

    • To develop a deep-learning-based super-resolution beam position estimator (DSRBPE).
    • To improve the accuracy of beam position estimation for weak optical signals in deep space.

    Main Methods:

    • Developed a deep-learning model incorporating photon data modeling.
    • Optimized a super-resolution convolutional neural network framework and loss function.
    • Utilized simulation and experimental validation.

    Main Results:

    • Achieved sub-pixel precision of 0.2 pixels for centroid extraction.
    • Successfully estimated the position of a weak 4x4 pixel Gaussian spot on a 32x32 detector.
    • Demonstrated superior robustness under low signal-to-noise ratio conditions compared to conventional algorithms.

    Conclusions:

    • DSRBPE significantly enhances the accuracy of beam position estimation in deep-space optical sensing.
    • The proposed method offers a robust solution for challenging low signal-to-noise environments.
    • Deep learning provides a powerful approach for improving optical sensing capabilities.